431 citations · 538 across the 4 of their papers we have counts for
4 papers
SeDe: Balancing Blockchain Privacy and Regulatory Compliance by Selective De-Anonymization
Amit Chaudhary, Hamish Ivey-Law
Privacy is one of the essential pillars for the widespread adoption of blockchains, but public blockchains are transparent by nature. Modern analytics techniques can easily subdue…
SEALion: a Framework for Neural Network Inference on Encrypted Data
Tim van Elsloo, Giorgio Patrini, Hamish Ivey-Law
We present SEALion: an extensible framework for privacy-preserving machine learning with homomorphic encryption. It allows one to learn deep neural networks that can be seamlessly…
Entity Resolution and Federated Learning get a Federated Resolution
Richard Nock, Stephen Hardy, Wilko Henecka +4
Consider two data providers, each maintaining records of different feature sets about common entities. They aim to learn a linear model over the whole set of features. This problem…
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law +4
Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, name…